File size: 4,944 Bytes
69591a9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 |
import PIL.Image
import streamlit as st
from dnafiber.data.utils import read_czi, read_tiff, preprocess
import cv2
import numpy as np
import math
from dnafiber.deployment import _get_model
import PIL
from PIL import Image
import io
import base64
MAX_WIDTH = 512
MAX_HEIGHT = 512
TYPE_MAPPING = {
0: "BG",
1: "SINGLE",
2: "BILATERAL",
3: "TRICOLOR",
4: "MULTICOLOR",
}
@st.cache_data
def load_image(_filepath, id=None):
filename = str(_filepath.name)
if filename.endswith(".czi"):
return read_czi(_filepath)
elif filename.endswith(".tif") or filename.endswith(".tiff"):
return read_tiff(_filepath)
elif (
filename.endswith(".png")
or filename.endswith(".jpg")
or filename.endswith(".jpeg")
):
image = PIL.Image.open(_filepath)
image = np.array(image)
return image
else:
raise NotImplementedError(f"File type {filename} is not supported yet")
@st.cache_data
def get_image(_filepath, reverse_channel, id):
filename = str(_filepath.name)
image = load_image(_filepath, id)
if filename.endswith(".czi") or filename.endswith(".tif") or filename.endswith(".tiff"):
image = preprocess(image, reverse_channel)
image = cv2.normalize(
image, None, 0, 255, cv2.NORM_MINMAX, dtype=cv2.CV_8U
)
return image
def get_multifile_image(_filepaths):
result = None
if _filepaths[0] is not None:
chan1 = get_image(_filepaths[0], False, _filepaths[0].file_id)
chan1 = cv2.cvtColor(chan1, cv2.COLOR_RGB2GRAY)
h, w = chan1.shape[:2]
else:
chan1 = None
if _filepaths[1] is not None:
chan2 = get_image(_filepaths[1], False, _filepaths[1].file_id)
chan2 = cv2.cvtColor(chan2, cv2.COLOR_RGB2GRAY)
h, w = chan2.shape[:2]
else:
chan2 = None
result = np.zeros((h, w, 3), dtype=np.uint8)
if chan1 is not None:
result[:, :, 0] = chan1
else:
result[:, :, 0] = chan2
if chan2 is not None:
result[:, :, 1] = chan2
else:
result[:, :, 1] = chan1
return result
def numpy_to_base64_png(image_array):
"""
Encodes a NumPy image array to a base64 string (PNG format).
Args:
image_array: A NumPy array representing the image.
Returns:
A base64 string representing the PNG image.
"""
# Convert NumPy array to PIL Image
image = Image.fromarray(image_array)
# Create an in-memory binary stream
buffer = io.BytesIO()
# Save the image to the buffer in PNG format
image.save(buffer, format="jpeg")
# Get the byte data from the buffer
png_data = buffer.getvalue()
# Encode the byte data to base64
base64_encoded = base64.b64encode(png_data).decode()
return f"data:image/jpeg;base64,{base64_encoded}"
@st.cache_data
def get_resized_image(_image, id):
h, w = _image.shape[:2]
if w > MAX_WIDTH:
scale = MAX_WIDTH / w
new_size = (int(w * scale), int(h * scale))
resized_image = cv2.resize(_image, new_size, interpolation=cv2.INTER_NEAREST)
else:
resized_image = _image
if h > MAX_HEIGHT:
scale = MAX_HEIGHT / h
new_size = (int(w * scale), int(h * scale))
resized_image = cv2.resize(
resized_image, new_size, interpolation=cv2.INTER_NEAREST
)
else:
resized_image = resized_image
return resized_image
def bokeh_imshow(fig, image):
# image is a numpy array of shape (h, w, 3) or (h, w) of type uint8
if len(image.shape) == 2:
# grayscale image
image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
# Convert to h*w with uint32
img = np.empty((image.shape[0], image.shape[1]), dtype=np.uint32)
view = img.view(dtype=np.uint8).reshape((image.shape[0], image.shape[1], 4)) # RGBA
view[:, :, 0] = image[:, :, 0]
view[:, :, 1] = image[:, :, 1]
view[:, :, 2] = image[:, :, 2]
view[:, :, 3] = 255 # Alpha channel
fig.image_rgba(image=[img], x=0, y=0, dw=image.shape[1], dh=image.shape[0])
@st.cache_resource
def get_model(device, revision=None):
return _get_model(revision=revision, device=device)
def pad_image_to_croppable(_image, bx, by, uid=None):
# Pad the image to be divisible by bx and by
h, w = _image.shape[:2]
if h % bx != 0:
pad_h = bx - (h % bx)
else:
pad_h = 0
if w % by != 0:
pad_w = by - (w % by)
else:
pad_w = 0
_image = cv2.copyMakeBorder(
_image,
math.ceil(pad_h / 2),
math.floor(pad_h / 2),
math.ceil(pad_w / 2),
math.floor(pad_w / 2),
cv2.BORDER_CONSTANT,
value=(0, 0, 0),
)
return _image
|